Overview

Dataset statistics

Number of variables20
Number of observations7905
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory160.0 B

Variable types

Numeric12
Categorical5
Boolean3

Alerts

n_days is highly overall correlated with ascitesHigh correlation
bilirubin is highly overall correlated with copperHigh correlation
copper is highly overall correlated with bilirubinHigh correlation
ascites is highly overall correlated with n_days and 1 other fieldsHigh correlation
hepatomegaly is highly overall correlated with stageHigh correlation
edema is highly overall correlated with ascitesHigh correlation
stage is highly overall correlated with hepatomegalyHigh correlation
sex is highly imbalanced (62.7%)Imbalance
ascites is highly imbalanced (72.2%)Imbalance
edema is highly imbalanced (65.7%)Imbalance
id is uniformly distributedUniform
id has unique valuesUnique

Reproduction

Analysis started2023-12-05 20:01:12.041276
Analysis finished2023-12-05 20:01:57.169548
Duration45.13 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct7905
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3952
Minimum0
Maximum7904
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size61.9 KiB
2023-12-05T20:01:57.392065image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile395.2
Q11976
median3952
Q35928
95-th percentile7508.8
Maximum7904
Range7904
Interquartile range (IQR)3952

Descriptive statistics

Standard deviation2282.1213
Coefficient of variation (CV)0.57745984
Kurtosis-1.2
Mean3952
Median Absolute Deviation (MAD)1976
Skewness0
Sum31240560
Variance5208077.5
MonotonicityStrictly increasing
2023-12-05T20:01:57.722873image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
5280 1
 
< 0.1%
5278 1
 
< 0.1%
5277 1
 
< 0.1%
5276 1
 
< 0.1%
5275 1
 
< 0.1%
5274 1
 
< 0.1%
5273 1
 
< 0.1%
5272 1
 
< 0.1%
5271 1
 
< 0.1%
Other values (7895) 7895
99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
7904 1
< 0.1%
7903 1
< 0.1%
7902 1
< 0.1%
7901 1
< 0.1%
7900 1
< 0.1%
7899 1
< 0.1%
7898 1
< 0.1%
7897 1
< 0.1%
7896 1
< 0.1%
7895 1
< 0.1%

n_days
Real number (ℝ)

Distinct461
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2030.1733
Minimum41
Maximum4795
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.9 KiB
2023-12-05T20:01:58.116891image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum41
5-th percentile334
Q11230
median1831
Q32689
95-th percentile4127
Maximum4795
Range4754
Interquartile range (IQR)1459

Descriptive statistics

Standard deviation1094.2337
Coefficient of variation (CV)0.53898539
Kurtosis-0.49401726
Mean2030.1733
Median Absolute Deviation (MAD)724
Skewness0.44865975
Sum16048520
Variance1197347.5
MonotonicityNot monotonic
2023-12-05T20:01:58.449698image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1216 117
 
1.5%
1434 105
 
1.3%
769 83
 
1.0%
3445 73
 
0.9%
1765 64
 
0.8%
1785 64
 
0.8%
1363 60
 
0.8%
904 59
 
0.7%
334 58
 
0.7%
2294 56
 
0.7%
Other values (451) 7166
90.7%
ValueCountFrequency (%)
41 13
0.2%
51 16
0.2%
71 14
0.2%
76 1
 
< 0.1%
77 21
0.3%
78 1
 
< 0.1%
108 1
 
< 0.1%
110 25
0.3%
121 1
 
< 0.1%
124 1
 
< 0.1%
ValueCountFrequency (%)
4795 7
 
0.1%
4556 51
0.6%
4523 15
 
0.2%
4509 41
0.5%
4500 28
0.4%
4467 14
 
0.2%
4459 19
 
0.2%
4453 22
0.3%
4427 14
 
0.2%
4392 1
 
< 0.1%

drug
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.9 KiB
Placebo
4010 
D-penicillamine
3895 

Length

Max length15
Median length7
Mean length10.941809
Min length7

Characters and Unicode

Total characters86495
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD-penicillamine
2nd rowPlacebo
3rd rowPlacebo
4th rowPlacebo
5th rowPlacebo

Common Values

ValueCountFrequency (%)
Placebo 4010
50.7%
D-penicillamine 3895
49.3%

Length

2023-12-05T20:01:58.734149image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-05T20:01:59.131974image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
placebo 4010
50.7%
d-penicillamine 3895
49.3%

Most occurring characters

ValueCountFrequency (%)
l 11800
13.6%
e 11800
13.6%
i 11685
13.5%
a 7905
9.1%
c 7905
9.1%
n 7790
9.0%
P 4010
 
4.6%
b 4010
 
4.6%
o 4010
 
4.6%
D 3895
 
4.5%
Other values (3) 11685
13.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 74695
86.4%
Uppercase Letter 7905
 
9.1%
Dash Punctuation 3895
 
4.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 11800
15.8%
e 11800
15.8%
i 11685
15.6%
a 7905
10.6%
c 7905
10.6%
n 7790
10.4%
b 4010
 
5.4%
o 4010
 
5.4%
p 3895
 
5.2%
m 3895
 
5.2%
Uppercase Letter
ValueCountFrequency (%)
P 4010
50.7%
D 3895
49.3%
Dash Punctuation
ValueCountFrequency (%)
- 3895
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 82600
95.5%
Common 3895
 
4.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 11800
14.3%
e 11800
14.3%
i 11685
14.1%
a 7905
9.6%
c 7905
9.6%
n 7790
9.4%
P 4010
 
4.9%
b 4010
 
4.9%
o 4010
 
4.9%
D 3895
 
4.7%
Other values (2) 7790
9.4%
Common
ValueCountFrequency (%)
- 3895
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 86495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 11800
13.6%
e 11800
13.6%
i 11685
13.5%
a 7905
9.1%
c 7905
9.1%
n 7790
9.0%
P 4010
 
4.6%
b 4010
 
4.6%
o 4010
 
4.6%
D 3895
 
4.5%
Other values (3) 11685
13.5%

age
Real number (ℝ)

Distinct391
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18373.146
Minimum9598
Maximum28650
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.9 KiB
2023-12-05T20:01:59.506732image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum9598
5-th percentile12307
Q115574
median18713
Q320684
95-th percentile24622
Maximum28650
Range19052
Interquartile range (IQR)5110

Descriptive statistics

Standard deviation3679.9587
Coefficient of variation (CV)0.20029007
Kurtosis-0.49738238
Mean18373.146
Median Absolute Deviation (MAD)2604
Skewness0.084091298
Sum1.4523972 × 108
Variance13542096
MonotonicityNot monotonic
2023-12-05T20:01:59.854443image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22369 79
 
1.0%
22388 71
 
0.9%
20684 71
 
0.9%
19060 70
 
0.9%
16279 66
 
0.8%
20459 65
 
0.8%
19246 62
 
0.8%
14161 62
 
0.8%
22960 61
 
0.8%
23331 61
 
0.8%
Other values (381) 7237
91.5%
ValueCountFrequency (%)
9598 18
0.2%
10550 17
0.2%
10795 7
 
0.1%
10810 1
 
< 0.1%
10958 1
 
< 0.1%
11058 33
0.4%
11167 10
 
0.1%
11273 19
0.2%
11330 1
 
< 0.1%
11462 19
0.2%
ValueCountFrequency (%)
28650 36
0.5%
28018 5
 
0.1%
27398 22
0.3%
27394 1
 
< 0.1%
27239 1
 
< 0.1%
27220 23
0.3%
26580 8
 
0.1%
26567 1
 
< 0.1%
26259 13
 
0.2%
25899 20
0.3%

sex
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.9 KiB
F
7336 
M
 
569

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7905
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowF
3rd rowF
4th rowF
5th rowF

Common Values

ValueCountFrequency (%)
F 7336
92.8%
M 569
 
7.2%

Length

2023-12-05T20:02:00.191247image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-05T20:02:00.444895image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
f 7336
92.8%
m 569
 
7.2%

Most occurring characters

ValueCountFrequency (%)
F 7336
92.8%
M 569
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 7905
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 7336
92.8%
M 569
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 7905
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 7336
92.8%
M 569
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7905
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 7336
92.8%
M 569
 
7.2%

ascites
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
False
7525 
True
 
380
ValueCountFrequency (%)
False 7525
95.2%
True 380
 
4.8%
2023-12-05T20:02:00.749953image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
True
4042 
False
3863 
ValueCountFrequency (%)
True 4042
51.1%
False 3863
48.9%
2023-12-05T20:02:01.005270image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

spiders
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
False
5966 
True
1939 
ValueCountFrequency (%)
False 5966
75.5%
True 1939
 
24.5%
2023-12-05T20:02:01.274245image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

edema
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.9 KiB
N
7161 
S
 
399
Y
 
345

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7905
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowY
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 7161
90.6%
S 399
 
5.0%
Y 345
 
4.4%

Length

2023-12-05T20:02:01.510203image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-05T20:02:01.771030image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
n 7161
90.6%
s 399
 
5.0%
y 345
 
4.4%

Most occurring characters

ValueCountFrequency (%)
N 7161
90.6%
S 399
 
5.0%
Y 345
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 7905
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 7161
90.6%
S 399
 
5.0%
Y 345
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 7905
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 7161
90.6%
S 399
 
5.0%
Y 345
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7905
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 7161
90.6%
S 399
 
5.0%
Y 345
 
4.4%

bilirubin
Real number (ℝ)

Distinct111
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5944845
Minimum0.3
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.9 KiB
2023-12-05T20:02:02.133778image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.5
Q10.7
median1.1
Q33
95-th percentile11
Maximum28
Range27.7
Interquartile range (IQR)2.3

Descriptive statistics

Standard deviation3.8129603
Coefficient of variation (CV)1.4696408
Kurtosis12.908824
Mean2.5944845
Median Absolute Deviation (MAD)0.5
Skewness3.3396953
Sum20509.4
Variance14.538666
MonotonicityNot monotonic
2023-12-05T20:02:02.440278image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6 847
 
10.7%
0.7 653
 
8.3%
0.8 613
 
7.8%
0.9 608
 
7.7%
0.5 552
 
7.0%
1.1 443
 
5.6%
1.3 368
 
4.7%
1 292
 
3.7%
0.4 180
 
2.3%
1.4 175
 
2.2%
Other values (101) 3174
40.2%
ValueCountFrequency (%)
0.3 52
 
0.7%
0.4 180
 
2.3%
0.5 552
7.0%
0.6 847
10.7%
0.7 653
8.3%
0.8 613
7.8%
0.9 608
7.7%
1 292
 
3.7%
1.1 443
5.6%
1.2 166
 
2.1%
ValueCountFrequency (%)
28 13
0.2%
25.5 13
0.2%
24.5 16
0.2%
22.5 16
0.2%
21.9 1
 
< 0.1%
21.6 19
0.2%
21.4 1
 
< 0.1%
20 4
 
0.1%
18 4
 
0.1%
17.9 9
0.1%

cholesterol
Real number (ℝ)

Distinct226
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean350.56192
Minimum120
Maximum1775
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.9 KiB
2023-12-05T20:02:02.819343image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum120
5-th percentile198
Q1248
median298
Q3390
95-th percentile646
Maximum1775
Range1655
Interquartile range (IQR)142

Descriptive statistics

Standard deviation195.37934
Coefficient of variation (CV)0.5573319
Kurtosis18.162327
Mean350.56192
Median Absolute Deviation (MAD)62
Skewness3.6796575
Sum2771192
Variance38173.088
MonotonicityNot monotonic
2023-12-05T20:02:03.192553image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
448 152
 
1.9%
248 151
 
1.9%
263 143
 
1.8%
298 138
 
1.7%
232 131
 
1.7%
260 120
 
1.5%
257 117
 
1.5%
316 110
 
1.4%
236 109
 
1.4%
280 106
 
1.3%
Other values (216) 6628
83.8%
ValueCountFrequency (%)
120 10
 
0.1%
127 18
 
0.2%
132 36
0.5%
134 1
 
< 0.1%
149 7
 
0.1%
151 9
 
0.1%
168 9
 
0.1%
172 19
 
0.2%
174 20
 
0.3%
175 58
0.7%
ValueCountFrequency (%)
1775 11
0.1%
1712 19
0.2%
1600 22
0.3%
1492 1
 
< 0.1%
1480 11
0.1%
1436 1
 
< 0.1%
1336 9
0.1%
1276 21
0.3%
1236 1
 
< 0.1%
1128 14
0.2%

albumin
Real number (ℝ)

Distinct160
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5483226
Minimum1.96
Maximum4.64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.9 KiB
2023-12-05T20:02:03.543052image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1.96
5-th percentile2.97
Q13.35
median3.58
Q33.77
95-th percentile4.08
Maximum4.64
Range2.68
Interquartile range (IQR)0.42

Descriptive statistics

Standard deviation0.34617081
Coefficient of variation (CV)0.097559002
Kurtosis1.3396217
Mean3.5483226
Median Absolute Deviation (MAD)0.21
Skewness-0.5611495
Sum28049.49
Variance0.11983423
MonotonicityNot monotonic
2023-12-05T20:02:03.958359image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.35 370
 
4.7%
3.6 368
 
4.7%
3.7 326
 
4.1%
3.85 255
 
3.2%
3.5 223
 
2.8%
3.77 217
 
2.7%
3.26 195
 
2.5%
3.65 183
 
2.3%
3.61 166
 
2.1%
3.2 161
 
2.0%
Other values (150) 5441
68.8%
ValueCountFrequency (%)
1.96 4
 
0.1%
2.1 4
 
0.1%
2.23 3
 
< 0.1%
2.27 4
 
0.1%
2.31 4
 
0.1%
2.33 16
 
0.2%
2.35 1
 
< 0.1%
2.43 50
0.6%
2.52 1
 
< 0.1%
2.53 9
 
0.1%
ValueCountFrequency (%)
4.64 20
0.3%
4.52 5
 
0.1%
4.4 14
 
0.2%
4.38 24
0.3%
4.34 1
 
< 0.1%
4.31 1
 
< 0.1%
4.3 42
0.5%
4.26 1
 
< 0.1%
4.24 12
 
0.2%
4.23 19
0.2%

copper
Real number (ℝ)

Distinct171
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.902846
Minimum4
Maximum588
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.9 KiB
2023-12-05T20:02:04.302477image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile14
Q139
median63
Q3102
95-th percentile231
Maximum588
Range584
Interquartile range (IQR)63

Descriptive statistics

Standard deviation75.899266
Coefficient of variation (CV)0.90460895
Kurtosis10.21299
Mean83.902846
Median Absolute Deviation (MAD)26
Skewness2.7017358
Sum663252
Variance5760.6986
MonotonicityNot monotonic
2023-12-05T20:02:04.657047image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67 311
 
3.9%
52 303
 
3.8%
39 216
 
2.7%
58 207
 
2.6%
75 188
 
2.4%
41 179
 
2.3%
13 172
 
2.2%
20 169
 
2.1%
44 154
 
1.9%
38 151
 
1.9%
Other values (161) 5855
74.1%
ValueCountFrequency (%)
4 12
 
0.2%
5 2
 
< 0.1%
9 53
 
0.7%
10 25
 
0.3%
11 60
 
0.8%
12 36
 
0.5%
13 172
2.2%
14 42
 
0.5%
15 11
 
0.1%
16 7
 
0.1%
ValueCountFrequency (%)
588 19
0.2%
558 7
 
0.1%
464 26
0.3%
456 1
 
< 0.1%
444 21
0.3%
412 13
 
0.2%
380 43
0.5%
358 21
0.3%
308 4
 
0.1%
290 20
0.3%

alk_phos
Real number (ℝ)

Distinct364
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1816.7452
Minimum289
Maximum13862.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.9 KiB
2023-12-05T20:02:05.042908image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum289
5-th percentile614
Q1834
median1181
Q31857
95-th percentile6064.8
Maximum13862.4
Range13573.4
Interquartile range (IQR)1023

Descriptive statistics

Standard deviation1903.7507
Coefficient of variation (CV)1.0478908
Kurtosis11.59975
Mean1816.7452
Median Absolute Deviation (MAD)460
Skewness3.1955577
Sum14361371
Variance3624266.6
MonotonicityNot monotonic
2023-12-05T20:02:05.361717image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
663 117
 
1.5%
1345 81
 
1.0%
7277 79
 
1.0%
944 78
 
1.0%
794 76
 
1.0%
645 76
 
1.0%
1636 76
 
1.0%
1052 75
 
0.9%
2276 63
 
0.8%
674 63
 
0.8%
Other values (354) 7121
90.1%
ValueCountFrequency (%)
289 32
0.4%
310 10
 
0.1%
369 21
0.3%
377 17
0.2%
414 8
 
0.1%
423 31
0.4%
453 26
0.3%
466 16
0.2%
516 12
 
0.2%
554 31
0.4%
ValueCountFrequency (%)
13862.4 15
0.2%
13486.2 1
 
< 0.1%
12258.8 26
0.3%
11552 11
0.1%
11320.2 15
0.2%
11046.6 12
0.2%
10795.4 1
 
< 0.1%
10396.8 22
0.3%
10165 11
0.1%
9933.2 3
 
< 0.1%

sgot
Real number (ℝ)

Distinct206
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean114.6046
Minimum26.35
Maximum457.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.9 KiB
2023-12-05T20:02:05.749248image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum26.35
5-th percentile54.25
Q175.95
median108.5
Q3137.95
95-th percentile198.4
Maximum457.25
Range430.9
Interquartile range (IQR)62

Descriptive statistics

Standard deviation48.790945
Coefficient of variation (CV)0.42573286
Kurtosis5.8167874
Mean114.6046
Median Absolute Deviation (MAD)31
Skewness1.5348057
Sum905949.38
Variance2380.5563
MonotonicityNot monotonic
2023-12-05T20:02:06.114262image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71.3 256
 
3.2%
57.35 247
 
3.1%
137.95 206
 
2.6%
120.9 198
 
2.5%
97.65 189
 
2.4%
170.5 184
 
2.3%
93 178
 
2.3%
128.65 170
 
2.2%
66.65 138
 
1.7%
106.95 137
 
1.7%
Other values (196) 6002
75.9%
ValueCountFrequency (%)
26.35 8
 
0.1%
28.38 12
 
0.2%
40.6 1
 
< 0.1%
41.85 16
 
0.2%
43.4 40
0.5%
45 14
 
0.2%
46.5 6
 
0.1%
49.6 52
0.7%
51.15 57
0.7%
52 15
 
0.2%
ValueCountFrequency (%)
457.25 17
0.2%
338 9
0.1%
328.6 15
0.2%
299.15 6
 
0.1%
288 9
0.1%
280.55 15
0.2%
272.8 9
0.1%
260.15 1
 
< 0.1%
253 1
 
< 0.1%
246.45 13
0.2%

tryglicerides
Real number (ℝ)

Distinct154
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.34016
Minimum33
Maximum598
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.9 KiB
2023-12-05T20:02:06.489555image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile56
Q184
median104
Q3139
95-th percentile210
Maximum598
Range565
Interquartile range (IQR)55

Descriptive statistics

Standard deviation52.530402
Coefficient of variation (CV)0.45543894
Kurtosis15.048118
Mean115.34016
Median Absolute Deviation (MAD)27
Skewness2.6339208
Sum911764
Variance2759.4431
MonotonicityNot monotonic
2023-12-05T20:02:06.803057image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 262
 
3.3%
85 223
 
2.8%
91 218
 
2.8%
118 211
 
2.7%
68 188
 
2.4%
56 187
 
2.4%
146 181
 
2.3%
108 175
 
2.2%
55 171
 
2.2%
133 170
 
2.2%
Other values (144) 5919
74.9%
ValueCountFrequency (%)
33 13
 
0.2%
44 37
 
0.5%
46 12
 
0.2%
49 13
 
0.2%
50 19
 
0.2%
52 24
 
0.3%
53 15
 
0.2%
55 171
2.2%
56 187
2.4%
57 10
 
0.1%
ValueCountFrequency (%)
598 13
0.2%
432 16
0.2%
393 1
 
< 0.1%
382 4
 
0.1%
322 5
 
0.1%
319 15
0.2%
318 18
0.2%
309 20
0.3%
283 1
 
< 0.1%
280 20
0.3%

platelets
Real number (ℝ)

Distinct227
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean265.22897
Minimum62
Maximum563
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.9 KiB
2023-12-05T20:02:07.147159image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum62
5-th percentile128
Q1211
median265
Q3316
95-th percentile430
Maximum563
Range501
Interquartile range (IQR)105

Descriptive statistics

Standard deviation87.465579
Coefficient of variation (CV)0.32977385
Kurtosis0.33057783
Mean265.22897
Median Absolute Deviation (MAD)53
Skewness0.42004793
Sum2096635
Variance7650.2274
MonotonicityNot monotonic
2023-12-05T20:02:07.539175image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
344 233
 
2.9%
228 159
 
2.0%
268 158
 
2.0%
295 154
 
1.9%
336 147
 
1.9%
251 144
 
1.8%
265 138
 
1.7%
269 136
 
1.7%
213 136
 
1.7%
309 132
 
1.7%
Other values (217) 6368
80.6%
ValueCountFrequency (%)
62 11
 
0.1%
65 1
 
< 0.1%
70 10
 
0.1%
71 15
 
0.2%
76 1
 
< 0.1%
79 18
0.2%
80 25
0.3%
81 11
 
0.1%
88 3
 
< 0.1%
95 38
0.5%
ValueCountFrequency (%)
563 36
0.5%
539 5
 
0.1%
518 14
 
0.2%
515 2
 
< 0.1%
514 13
 
0.2%
493 17
0.2%
487 10
 
0.1%
474 17
0.2%
471 24
0.3%
467 40
0.5%

prothrombin
Real number (ℝ)

Distinct49
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.629462
Minimum9
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.9 KiB
2023-12-05T20:02:07.851897image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile9.6
Q110
median10.6
Q311
95-th percentile12
Maximum18
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.78173483
Coefficient of variation (CV)0.073544155
Kurtosis4.288955
Mean10.629462
Median Absolute Deviation (MAD)0.5
Skewness1.292436
Sum84025.9
Variance0.61110934
MonotonicityNot monotonic
2023-12-05T20:02:08.233838image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
10.6 1070
 
13.5%
11 842
 
10.7%
10 638
 
8.1%
9.9 517
 
6.5%
9.8 440
 
5.6%
10.1 390
 
4.9%
10.9 339
 
4.3%
11.5 295
 
3.7%
9.6 288
 
3.6%
10.2 283
 
3.6%
Other values (39) 2803
35.5%
ValueCountFrequency (%)
9 8
 
0.1%
9.1 9
 
0.1%
9.2 5
 
0.1%
9.3 8
 
0.1%
9.4 17
 
0.2%
9.5 137
 
1.7%
9.6 288
3.6%
9.7 199
 
2.5%
9.8 440
5.6%
9.9 517
6.5%
ValueCountFrequency (%)
18 1
 
< 0.1%
17.1 2
 
< 0.1%
15.2 12
 
0.2%
14.1 4
 
0.1%
13.6 9
 
0.1%
13.4 1
 
< 0.1%
13.3 6
 
0.1%
13.2 32
0.4%
13.1 1
 
< 0.1%
13 45
0.6%

stage
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size61.9 KiB
3.0
3153 
4.0
2703 
2.0
1652 
1.0
397 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters23715
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row4.0
4th row3.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.0 3153
39.9%
4.0 2703
34.2%
2.0 1652
20.9%
1.0 397
 
5.0%

Length

2023-12-05T20:02:08.594857image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-05T20:02:08.983814image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
3.0 3153
39.9%
4.0 2703
34.2%
2.0 1652
20.9%
1.0 397
 
5.0%

Most occurring characters

ValueCountFrequency (%)
. 7905
33.3%
0 7905
33.3%
3 3153
 
13.3%
4 2703
 
11.4%
2 1652
 
7.0%
1 397
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15810
66.7%
Other Punctuation 7905
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7905
50.0%
3 3153
 
19.9%
4 2703
 
17.1%
2 1652
 
10.4%
1 397
 
2.5%
Other Punctuation
ValueCountFrequency (%)
. 7905
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23715
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 7905
33.3%
0 7905
33.3%
3 3153
 
13.3%
4 2703
 
11.4%
2 1652
 
7.0%
1 397
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23715
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 7905
33.3%
0 7905
33.3%
3 3153
 
13.3%
4 2703
 
11.4%
2 1652
 
7.0%
1 397
 
1.7%

status
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.9 KiB
C
4965 
D
2665 
CL
 
275

Length

Max length2
Median length1
Mean length1.0347881
Min length1

Characters and Unicode

Total characters8180
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD
2nd rowC
3rd rowD
4th rowC
5th rowC

Common Values

ValueCountFrequency (%)
C 4965
62.8%
D 2665
33.7%
CL 275
 
3.5%

Length

2023-12-05T20:02:09.327760image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-05T20:02:09.699220image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
c 4965
62.8%
d 2665
33.7%
cl 275
 
3.5%

Most occurring characters

ValueCountFrequency (%)
C 5240
64.1%
D 2665
32.6%
L 275
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 8180
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 5240
64.1%
D 2665
32.6%
L 275
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 8180
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 5240
64.1%
D 2665
32.6%
L 275
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8180
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 5240
64.1%
D 2665
32.6%
L 275
 
3.4%

Interactions

2023-12-05T20:01:52.375945image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:14.927514image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:18.464533image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:21.742527image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:25.191920image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:28.324805image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:31.823596image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:35.073403image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:38.423155image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:42.195360image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:45.234903image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:48.667250image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:52.672876image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:15.491578image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:18.764974image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:22.070901image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:25.477391image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:28.590766image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:32.119109image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:35.386502image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:38.732961image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:42.465311image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:45.570276image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:49.017180image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:52.927346image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:15.734140image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:19.025495image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:22.379771image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:25.735423image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:28.853660image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:32.413325image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:35.665776image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:39.051996image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:42.691273image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:45.866529image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:49.274768image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:53.212055image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:15.981229image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:19.308509image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:22.703620image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:26.016133image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:29.115206image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:32.683678image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:35.942720image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:39.414496image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:42.951897image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:46.193568image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:49.532486image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:53.470515image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:16.282897image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:19.601613image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:22.932665image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:26.234869image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:29.362068image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:32.908307image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:36.211126image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:39.683730image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:43.165985image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:46.476213image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:49.797643image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:53.765559image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:16.547754image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:19.874058image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:23.224793image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:26.498115image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:29.626849image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:33.185400image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:36.501751image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:40.028665image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:43.443807image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:46.777546image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:50.104725image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:54.039152image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:16.801886image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:20.163191image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:23.464093image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:26.771109image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:29.882809image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:33.423116image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:36.765622image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:40.289790image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:43.686444image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:47.042665image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:50.324748image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:54.307728image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:17.070925image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:20.420424image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:23.722327image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:27.017761image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:30.198698image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:33.701437image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:37.059124image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:40.627916image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:43.958451image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:47.345189image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:50.916894image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:54.581183image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:17.389236image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:20.703259image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:24.066030image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:27.283661image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:30.783163image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:34.017139image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:37.388277image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:41.064909image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:44.276795image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:47.648810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:51.231755image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:54.837059image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:17.654106image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:20.940053image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:24.351777image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:27.508211image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:31.031891image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:34.242802image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:37.661884image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:41.332763image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:44.534762image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:47.882930image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:51.503710image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:55.150813image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:17.964120image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:21.229601image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:24.629347image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:27.777716image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:31.301958image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:34.551598image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:37.903542image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:41.644864image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:44.808540image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:48.146777image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:51.775517image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:55.400404image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:18.237320image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:21.466331image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:24.903762image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:28.024004image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:31.561689image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:34.783882image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:38.156722image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:41.920542image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:45.005798image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:48.436072image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-05T20:01:52.065413image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-12-05T20:02:09.987162image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
idn_daysagebilirubincholesterolalbumincopperalk_phossgottrygliceridesplateletsprothrombindrugsexasciteshepatomegalyspidersedemastagestatus
id1.000-0.010-0.0100.013-0.009-0.017-0.0010.0050.0210.001-0.0040.0130.0000.0240.0220.0240.0000.0000.0190.000
n_days-0.0101.000-0.104-0.405-0.1230.241-0.338-0.147-0.281-0.2090.155-0.1500.0530.0860.5120.3270.2710.3710.1750.349
age-0.010-0.1041.0000.055-0.077-0.0790.034-0.041-0.0370.021-0.0970.1340.1290.1460.1910.1260.0820.1400.1040.173
bilirubin0.013-0.4050.0551.0000.325-0.3040.5860.3330.4990.316-0.1680.2680.0840.1060.4390.3350.3210.3010.1370.348
cholesterol-0.009-0.123-0.0770.3251.000-0.0540.2550.3200.3470.3320.124-0.0500.0790.0490.0760.1360.0720.0330.0350.156
albumin-0.0170.241-0.079-0.304-0.0541.000-0.236-0.167-0.220-0.1130.125-0.1670.1050.0600.4430.2680.2300.3160.1530.223
copper-0.001-0.3380.0340.5860.255-0.2361.0000.2810.4380.341-0.1260.2090.0720.1790.3340.3120.2820.2400.1380.324
alk_phos0.005-0.147-0.0410.3330.320-0.1670.2811.0000.4270.1950.0520.0920.0530.0360.1220.2100.0990.1140.0630.171
sgot0.021-0.281-0.0370.4990.347-0.2200.4380.4271.0000.186-0.0360.1340.0810.0760.1430.2330.1910.1140.0930.242
tryglicerides0.001-0.2090.0210.3160.332-0.1130.3410.1950.1861.000-0.0130.0080.0570.1140.2130.2190.1370.1040.0590.160
platelets-0.0040.155-0.097-0.1680.1240.125-0.1260.052-0.036-0.0131.000-0.1790.0660.0570.2870.2210.2120.1890.1340.172
prothrombin0.013-0.1500.1340.268-0.050-0.1670.2090.0920.1340.008-0.1791.0000.0400.0890.3450.3070.3110.2970.1980.301
drug0.0000.0530.1290.0840.0790.1050.0720.0530.0810.0570.0660.0401.0000.0430.0450.0620.0000.0330.0270.022
sex0.0240.0860.1460.1060.0490.0600.1790.0360.0760.1140.0570.0890.0431.0000.0330.0650.0240.0700.0380.130
ascites0.0220.5120.1910.4390.0760.4430.3340.1220.1430.2130.2870.3450.0450.0331.0000.1840.2090.6690.1950.276
hepatomegaly0.0240.3270.1260.3350.1360.2680.3120.2100.2330.2190.2210.3070.0620.0650.1841.0000.3290.2270.5260.396
spiders0.0000.2710.0820.3210.0720.2300.2820.0990.1910.1370.2120.3110.0000.0240.2090.3291.0000.2660.3080.324
edema0.0000.3710.1400.3010.0330.3160.2400.1140.1140.1040.1890.2970.0330.0700.6690.2270.2661.0000.1710.241
stage0.0190.1750.1040.1370.0350.1530.1380.0630.0930.0590.1340.1980.0270.0380.1950.5260.3080.1711.0000.273
status0.0000.3490.1730.3480.1560.2230.3240.1710.2420.1600.1720.3010.0220.1300.2760.3960.3240.2410.2731.000

Missing values

2023-12-05T20:01:55.815249image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-05T20:01:56.770206image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idn_daysdrugagesexasciteshepatomegalyspidersedemabilirubincholesterolalbumincopperalk_phossgottrygliceridesplateletsprothrombinstagestatus
00999D-penicillamine21532MNNNN2.300000316.0000003.350000172.0000001601.000000179.80000063.000000394.0000009.7000003.000000D
112574Placebo19237FNNNN0.900000364.0000003.54000063.0000001440.000000134.85000088.000000361.00000011.0000003.000000C
223428Placebo13727FNYYY3.300000299.0000003.550000131.0000001029.000000119.35000050.000000199.00000011.7000004.000000D
332576Placebo18460FNNNN0.600000256.0000003.50000058.0000001653.00000071.30000096.000000269.00000010.7000003.000000C
44788Placebo16658FNYNN1.100000346.0000003.65000063.0000001181.000000125.55000096.000000298.00000010.6000004.000000C
55703D-penicillamine19270FNYNN0.600000227.0000003.46000034.0000006456.20000060.63000068.000000213.00000011.5000003.000000D
661300Placebo17703FNNNN1.000000328.0000003.35000043.0000001677.000000137.95000090.000000291.0000009.8000003.000000C
771615Placebo21281FNYNN0.600000273.0000003.94000036.000000598.00000052.700000214.000000227.0000009.9000003.000000C
882050D-penicillamine20684FNNNN0.700000360.0000003.65000072.0000003196.00000094.550000154.000000269.0000009.8000002.000000C
992615D-penicillamine15009FNNNN0.900000478.0000003.60000039.0000001758.000000171.000000140.000000234.00000010.6000002.000000C
idn_daysdrugagesexasciteshepatomegalyspidersedemabilirubincholesterolalbumincopperalk_phossgottrygliceridesplateletsprothrombinstagestatus
789578951433Placebo14161FNNNN0.500000291.0000004.24000037.0000001065.00000085.250000195.000000201.00000010.6000002.000000C
789678961271Placebo13806FNNNN0.600000328.0000003.95000031.000000663.00000052.700000166.000000344.00000010.4000003.000000C
789778971455Placebo16898FNNYN3.400000279.0000003.530000143.000000671.000000113.15000072.000000151.0000009.8000003.000000C
7898789877Placebo19884FYYNY5.100000178.0000002.750000464.0000001020.000000120.900000118.00000080.00000012.3000004.000000D
789978991413Placebo24622FNNNN1.300000262.0000003.73000065.0000002045.00000089.90000078.000000181.00000011.0000003.000000D
790079001166D-penicillamine16839FNNNN0.800000309.0000003.56000038.0000001629.00000079.050000224.000000344.0000009.9000002.000000C
790179011492Placebo17031FNYNN0.900000260.0000003.43000062.0000001440.000000142.00000078.000000277.00000010.0000004.000000C
790279021576D-penicillamine25873FNNYS2.000000225.0000003.19000051.000000933.00000069.75000062.000000200.00000012.7000002.000000D
790379033584D-penicillamine22960MNYNN0.700000248.0000002.75000032.0000001003.00000057.350000118.000000221.00000010.6000004.000000D
790479041978D-penicillamine19237FNNNN0.700000256.0000003.23000022.000000645.00000074.40000085.000000336.00000010.3000003.000000C